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A notebook to visualize the dbCAN search results on a phylogenetic tree¶

import sys
import os
from dotenv import load_dotenv, find_dotenv # do 'pip install python-dotenv'
from os.path import join, dirname, basename, exists, isdir


### Load environmental variables from the project root directory ###
# find .env automagically by walking up directories until it's found
dotenv_path = find_dotenv()

# load up the entries as environment variables
load_dotenv(dotenv_path)

# now you can get the variables using their names

# Check whether a network drive has been specified
DATABASE = os.environ.get("NETWORK_URL")
if DATABASE == 'None':
    pass
else:
    pass
    #mount network drive here

# set up directory paths
CURRENT_DIR = os.getcwd()
PROJ = dirname(dotenv_path) # project root directory

DATA = join(PROJ, 'data') #data directory
RAW_EXTERNAL = join(DATA, 'raw_external') # external data raw directory
RAW_INTERNAL = join(DATA, 'raw_internal') # internal data raw directory
INTERMEDIATE = join(DATA, 'intermediate') # intermediate data directory
FINAL = join(DATA, 'final') # final data directory
FIGURES = join(PROJ, 'figures') # figure output directory

#from orgtools import topfunctions, org_tax, uid_tax
from ete3 import Tree, NodeStyle, TextFace, TreeStyle
import ete3

import pandas as pd
import matplotlib
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np

import scipy
import scipy.cluster.hierarchy as sch

font = {'family' : 'DejaVu Sans',
        'weight' : 'normal',
        'size'   : 8}

matplotlib.rc('font', **font)

print('Python version: %s' % (sys.version))
print('Pandas version: %s' % pd.__version__)
print('Matplotlib version: %s' % matplotlib.__version__)
print('ETE3 version: %s' % ete3.__version__)

Python version: 3.7.7 (default, Sep 11 2020, 20:43:12) 
[GCC 7.3.0]
Pandas version: 1.0.3
Matplotlib version: 3.3.2
ETE3 version: 3.1.2

Visualize strain enzyme data¶

First load the data¶

df = pd.read_csv(join(FINAL, '332_yeast_genomes_enzyme_info_version_3.tsv'), sep='\t')

display(df.dtypes)
display(df.head())
display(df.tail())
display(df.describe())

organism                object
gene                    object
hmm_model               object
hmm_model_len            int64
hmm_match_from           int64
hmm_match_to             int64
hmm_match_coverage     float64
match_evalue           float64
gene_match_from          int64
gene_match_to            int64
enzyme                  object
family                  object
enzyme_type             object
signal_peptide          object
signal_peptide_prob    float64
sp_cut_pos              object
sp_cut_seq              object
sp_cut_prob            float64
genes_in_fasta           int64
dtype: object

General plot on how many there are of each kind of enzyme¶

display(df.groupby(['enzyme_type'])['gene'].count())

df.groupby(['enzyme_type'])['gene'].count().plot(kind='bar')
plt.ylabel('Total identified (#)')

enzyme_type
AA      4146
CBM     1939
CE      2166
GH     18653
GT     23334
PL        81
Name: gene, dtype: int64

Text(0, 0.5, 'Total identified (#)')

Load existing phylogenetic tree¶

Get phylogenetic tree from tempo and mode of genome evolution paper¶

Paper doi: 10.1016/j.cell.2018.10.023

Data doi: 10.6084/m9.figshare.5854692

Figshare link: https://figshare.com/articles/Tempo_and_mode_of_genome_evolution_in_the_budding_yeast_subphylum/5854692

def norm_organism(orgname):
    '''
    Return abbreviated organism name
    '''
    orgname = orgname.replace('[', '').replace(']', '')
    
    # two organisms occur twice, deal with these special cases
    if orgname == 'Metschnikowia matae var. maris':
        return 'metschnikowia_matae_maris'
    
    elif orgname == 'Nadsonia fulvescens var. elongata':
        return 'nadsonia_fulvescens_var_elongata'
      
    # now parse filename for the others
    if orgname.startswith('yH'):
        organism = '_'.join(orgname.split()[1:3]).lower().replace('.', '')
    else:
        organism = '_'.join(orgname.split()[:2]).lower().replace('.', '')
    
    return organism


def translate_organism(orgname):
    '''
    The organism names are different between 
    my data file and that used in the phylogenetic tree.
    Translate my names to the ones used in the tree.
    '''
    dictionary = {'nadsonia_fulvescens':'nadsonia_fulvescens_var_fulvescens',
                 'magnusiomyces_tetrasperma':'magnusiomyces_tetraspermus',
                 'candida_versatilis':'wickerhamiella_versatilis',
                 'candida_infanticola':'wickerhamiella_infanticola',
                 'candida_salmanticensis':'groenewaldozyma_salmanticensis',
                 'hanseniaspora_vinae':'hanseniaspora_vineae',
                 'candida_apicola':'starmerella_apicola',
                 'arxula_adeninivorans':'blastobotrys_adeninivorans',
                 'blastobotrys_raffinofermentans':'blastobotrys_raffinosifermentans',
                 'candida_homilentoma':'hyphopichia_homilentoma',
                 'candida_azyma':'wickerhamomyces_sp_yb_2243',
                 'candida_tenuis':'yamadazyma_tenuis',
                 'nakaseomyces_castellii':'candida_castellii',
                 'ogataea_populiabae':'ogataea_populialbae',
                 'nakaseomyces_bracarensis':'candida_bracarensis',
                 'candida_pyralidae':'suhomyces_pyralidae',
                 'candida_gatunensis':'teunomyces_gatunensis',
                 'dekkera_bruxellensis':'brettanomyces_bruxellensis',
                 'nakaseomyces_nivariensis':'candida_nivariensis',
                 'candida_tanzawaensis':'suhomyces_tanzawaensis',
                 'candida_kruisii':'teunomyces_kruisii',
                 'candida_cretensis':'teunomyces_cretensis',
                 'metschnikowia_bicuspidata':'metschnikowia_bicuspidata_var_bicuspidata',
                 'lachancea_fantastica':'lachancea_fantastica_nom_nud',
                 'candida_emberorum':'suhomyces_emberorum',
                 'candida_canberraensis':'suhomyces_canberraensis',
                 'ogataea_philodendra':'ogataea_philodendri',
                 'metschnikowia_dekortum':'metschnikowia_dekortorum',
                 'metschnikowia_lockheadii':'metschnikowia_lochheadii',
                 'metschnikowia_matae_maris':'metschnikowia_matae_var_maris',
                 'metschnikowia_matae':'metschnikowia_matae_var_matae'}
    
    return dictionary.get(orgname)


def load_tree():
    filepath = join(RAW_EXTERNAL, 'phylo_tree', '332_2408OGs_time-calibrated_phylogeny_species-names_updated.newick')

    tree = Tree(filepath)

    ts = TreeStyle()
    ts.mode = "c"
    ts.scale = 175
    ts.show_scale = False
    
    return tree, ts


tree, ts = load_tree()

# save tree with the updated names
#tree.write(format=1, outfile=join('phylo_tree', '332_genomes_my_names.newick'))

tree.render("%%inline", dpi=600, w=2400, tree_style=ts)

Remove GT and count how many families are found in each organism¶

def color_tree(tree, data, cmap):
    # now add in keys for the alternate org names
    for name in sorted(data.keys()):
        alt_name = translate_organism(name)
        if alt_name is not None:
            data[alt_name] = data[name]
            
    orgs_with_data = data.keys()

    # setup a way to normalize values to the colormap
    norm = matplotlib.colors.Normalize(vmin=min(data.values()), vmax=max(data.values()))
    
    # go through and color the nodes according to how many enzymes they have
    for node in tree.traverse():
        # Hide node circles
        #node.img_style['size'] = 0

        if node.is_leaf():
            name = norm_organism(node.name)

            if not name in orgs_with_data:
                raise ValueError

            rgba = cmap(norm(data[name]))
            color = matplotlib.colors.to_hex(rgba, keep_alpha=False)


            # change the color of the leaf
            nstyle = NodeStyle()
            
#             nstyle["bgcolor"] = color
#             nstyle["size"] = 0

            nstyle["fgcolor"] = color
            nstyle["size"] = 15

            # add teh color
            node.set_style(nstyle)
            
            # make the name pretty
            node.name = name.replace('_', ' ').capitalize()
            
        else:
            # change the color of the node
            nstyle = NodeStyle()
            
            nstyle["size"] = 0    
            
            # apply
            node.set_style(nstyle)
            
    return tree
        
    
        

tree, ts = load_tree()
    
    
# count the families per organism (with excluding GT)
data = df[df.enzyme_type != 'GT'].groupby('organism')['family'].count().reset_index()

# convert data to a dictionary with organism keys and data values
data = data.set_index('organism')['family'].to_dict()

# setup colormap
cmap = matplotlib.cm.get_cmap('YlOrRd')
  
# add color to the tree
tree = color_tree(tree, data, cmap)



# define output filename
filename = '332_tree'

# save the colormap (scale) for this plot
mat = np.array([[min(data.values())], [max(data.values())]])
img = plt.imshow(mat, origin="lower", cmap=cmap, interpolation='nearest')
plt.xticks([])
plt.yticks([])
cbar = plt.colorbar()
cbar.set_label('Enzyme families (#)', rotation=270, labelpad=15, fontsize=10)

plt.savefig(join(FIGURES, '{}_colorbar.png'.format(filename)))
plt.savefig(join(FIGURES, '{}_colorbar.pdf'.format(filename)))

# save pdf
tree.render(join(FIGURES, "{}.pdf".format(filename)), w=2400, tree_style=ts)

# save png
tree.render(join(FIGURES, "{}.png".format(filename)), w=2400, dpi=600, tree_style=ts)

# plot it
tree.render("%%inline", w=2400, dpi=100, tree_style=ts)

Make heatmap with substrates¶

# first I need to group the families according to the substrate they use
family_df = pd.read_csv(join(FINAL, '332_yeast_genomes_heatmap_data.tsv'), sep='\t')


# create a new data frame and set the organism name as index
substrate_df = pd.DataFrame(family_df.organism.values)
substrate_df.columns = ['Name']


# define substrate families
substrates = {'xylan':['GH3','GH5','GH11','GH30','GH43','GH51','GH62','GH67','GH115','CE1','CE3','CE4','CE5','CE12','CE15','CE16','CBM13'],
             'mannan':['GH2','GH5','GH26','GH27','GH76','GH125','GH134'],
             'xyloglucan':['GH2','GH3','GH5','GH12','GH16','GH29','GH31','GH35','GH42','GH45','GH74','GH95'],
             'beta-glucan':['GH3','GH16','GH55'],
             'lignin':['AA1','AA2','CE1','CE15'],
             'cellulose':['AA3','AA9','GH1','GH3','GH5','GH7','GH8','GH12','GH26','GH45','GH51','GH74','CBM1'],
             'chitin':['GH7','GH8','GH16','GH18','GH20','GH75','AA11','CE4','CBM18','CBM19','CBM32','CBM50'],
             'pectin':['PL1','PL3','PL4','PL26','GH28','GH78','CE8','GH106','GH139'],
             'starch':['GH13','GH15','GH31','GH63','CBM21'],}

#GH46???

# add in substrates
for sub in sorted(substrates.keys()):
    substrate_df[sub] = family_df[substrates[sub]].sum(axis=1)

    
    
#### Try to make organism names in substrate data match those in the tree #####
    
# go through and change the names to the alternate ones

for node in tree.traverse():
    if node.is_leaf():
        tree_name = node.name
        tree_norm_name = norm_organism(tree_name)
        

        for subst_name in substrate_df.Name:
            subst_alt_name = translate_organism(subst_name)
            
            if subst_alt_name is not None:
                search_name = subst_alt_name
            else:
                search_name = subst_name
                
            if search_name == tree_norm_name:
#                 print(subst_name, tree_name)
                substrate_df.loc[substrate_df.Name == subst_name, 'Name'] = tree_name

                
# change the index column
substrate_df = substrate_df.set_index('Name')


    
display(substrate_df.head())
display(substrate_df.tail())
display(substrate_df.describe())

# save data
substrate_df.to_csv(join(FINAL, '332_yeast_genomes_heatmap_data_substrates_tree_names.tsv'), sep='\t', index_label='#Names')

Plot all the organisms¶

# get the organism names and substrate names
y_labels = [s.replace('_', ' ').capitalize() for s in substrate_df.index]
x_labels = [s.capitalize() for s in substrate_df.columns]

# setup the heatmap
fig, ax = plt.subplots(1,1, figsize=(0.25*len(x_labels), 0.25*len(y_labels)))
img = ax.imshow(substrate_df.to_numpy(), cmap='YlOrRd')

# add names 
ax.xaxis.tick_top()
ax.set_xticks(np.arange(0, len(x_labels)))
ax.set_yticks(np.arange(0, len(y_labels)))
ax.set_xticklabels(x_labels, rotation=90)
ax.set_yticklabels(y_labels)

# add the colorbar
cbar = fig.colorbar(img, shrink=0.35)
cbar.set_label('Substrate families (#)', rotation=270, labelpad=15, fontsize=10)

filename = 'all_orgs_substrate_families'
plt.savefig(join(FIGURES, '{}.png'.format(filename)))
plt.savefig(join(FIGURES, '{}.pdf'.format(filename)))

Plot only the selected organisms¶

# chosen_orgs = ['aciculoconidium_aculeatum',
#          'ambrosiozyma_ambrosiae',
#          'ambrosiozyma_monospora',
#          'ambrosiozyma_oregonensis',
#          'ambrosiozyma_philentoma',
#          'ascoidea_asiatica',
#          'ascoidea_rubescens',
#          'arxula_adeninivorans', # actually 'blastobotrys_adeninivorans'
#          'blastobotrys_americana',
#          'blastobotrys_mokoenaii',
#          'blastobotrys_muscicola',
#          'blastobotrys_nivea',
#          'blastobotrys_peoriensis',
#          'blastobotrys_proliferans',
#          'blastobotrys_raffinofermentans',
#          'blastobotrys_serpentis',
#          'candida_gorgasii',
#          'debaryomyces_fabryi',
#          'debaryomyces_maramus',
#          'debaryomyces_nepalensis',
#          'debaryomyces_prosopidis',
#          'debaryomyces_subglobosus',
#          'diddensiella_caesifluorescens',
#          'lipomyces_arxii',
#          'lipomyces_doorenjongii',
#          'lipomyces_kononenkoae',
#          'lipomyces_lipofer',
#          'lipomyces_starkeyi',
#          'saccharomycopsis_capsularis',
#          'saccharomycopsis_malanga',
#          'scheffersomyces_lignosus',
#          'scheffersomyces_stipitis',
#          'spathaspora_passalidarum',
#          'spencermartinsiella_europaea',
#          'sugiyamaella_lignohabitans',
#          'wickerhamomyces_anomalus',
#          'wickerhamomyces_canadensis',
#          'wickerhamomyces_ciferrii',
#          'wickerhamomyces_hampshirensis']



chosen_orgs = ['lipomyces_starkeyi',
                 'lipomyces_arxii',
                 'lipomyces_kononenkoae',
                 'lipomyces_lipofer',
                 'lipomyces_doorenjongii',
                 'spencermartinsiella_europaea',
                 'sugiyamaella_lignohabitans',
                 'diddensiella_caesifluorescens',
                 'blastobotrys_peoriensis',
                 'blastobotrys_nivea',
                 'blastobotrys_muscicola',
                 'blastobotrys_mokoenaii',
                 'blastobotrys_americana',
                 'arxula_adeninivorans',
                 'blastobotrys_proliferans',
                 'blastobotrys_serpentis',
                 'blastobotrys_raffinofermentans',
                 'ambrosiozyma_philentoma',
                 'ambrosiozyma_ambrosiae',
                 'ambrosiozyma_monospora',
                 'ambrosiozyma_oregonensis',
                 'spathaspora_passalidarum',
                 'debaryomyces_subglobosus',
                 'debaryomyces_fabryi',
                 'debaryomyces_prosopidis',
                 'debaryomyces_hansenii',
                 'debaryomyces_nepalensis',
                 'candida_gorgasii',
                 'debaryomyces_maramus',
                 'scheffersomyces_lignosus',
                 'scheffersomyces_stipitis',
                 'aciculoconidium_aculeatum',
                 'wickerhamomyces_anomalus',
                 'wickerhamomyces_ciferrii',
                 'wickerhamomyces_canadensis',
                 'wickerhamomyces_hampshirensis',
                 'saccharomycopsis_capsularis',
                 'saccharomycopsis_malanga',
                 'ascoidea_asiatica',
                 'ascoidea_rubescens']

# first I need to group the families according to the substrate they use
family_df = pd.read_csv(join(FINAL, '332_yeast_genomes_heatmap_data.tsv'), sep='\t')


# create a new data frame and set the organism name as index
substrate_df = pd.DataFrame(family_df.organism.values)
substrate_df.columns = ['Name']



# add in substrates
for sub in sorted(substrates.keys()):
    substrate_df[sub] = family_df[substrates[sub]].sum(axis=1)



# change the index column
substrate_df = substrate_df.set_index('Name')
    
display(substrate_df.head())
display(substrate_df.tail())
display(substrate_df.describe())




# save data
substrate_df.to_csv(join(FINAL, '332_yeast_genomes_heatmap_data_substrates.tsv'), sep='\t', index_label='#Names')

for org in chosen_orgs:
    if org not in substrate_df.index:
        [print(org)]

substrate_df_chosen = substrate_df.loc[chosen_orgs]

# get the organism names and substrate names
y_labels = [s.replace('arxula_adeninivorans', 'blastobotrys_adeninivorans').replace('_', ' ').capitalize() 
            for s in substrate_df_chosen.index]
x_labels = [s.capitalize() for s in substrate_df.columns]

# setup the heatmap
fig, ax = plt.subplots(1,1, figsize=(0.25*len(x_labels), 0.25*len(y_labels)))
img = ax.imshow(substrate_df_chosen.to_numpy(), cmap='YlOrRd')

# add names 
ax.xaxis.tick_top()
ax.set_xticks(np.arange(0, len(x_labels)))
ax.set_yticks(np.arange(0, len(y_labels)))
ax.set_xticklabels(x_labels, rotation=90)
ax.set_yticklabels(y_labels)

# add the colorbar
cbar = fig.colorbar(img, shrink=0.35)
cbar.set_label('Substrate families (#)', rotation=270, labelpad=15, fontsize=10)

filename = 'selected_orgs_substrate_families'
plt.savefig(join(FIGURES, '{}.png'.format(filename)), pad_inches=3)
plt.savefig(join(FIGURES, '{}.pdf'.format(filename)), pad_inches=3)

	organism	gene	hmm_model	hmm_model_len	hmm_match_from	hmm_match_to	hmm_match_coverage	match_evalue	gene_match_from	gene_match_to	enzyme	family	enzyme_type	signal_peptide	signal_peptide_prob	sp_cut_pos	sp_cut_seq	sp_cut_prob	genes_in_fasta
0	starmerella_bombicola	-augustus-gene-0.1-mRNA-1_1_gene=maker-gi\|1002...	GT15.hmm	273	4	272	0.98	2.500000e-218	47	310	GT15	GT15	GT	OTHER	0.055799	NaN	NaN	NaN	4704
1	starmerella_bombicola	-augustus-gene-0.1-mRNA-1_1_gene=maker-gi\|1002...	GT15.hmm	273	1	272	0.99	2.500000e-218	442	712	GT15	GT15	GT	OTHER	0.055799	NaN	NaN	NaN	4704
2	candida_succiphila	-augustus-gene-0.117-mRNA-1_1_gene=maker-gi\|10...	GH5_9.hmm	303	1	303	1.00	2.500000e-119	561	873	GH5_9	GH5	GH	OTHER	0.001219	NaN	NaN	NaN	5247
3	saprochaete_clavata	-augustus-gene-0.118-mRNA-1_1_gene=maker-gi\|73...	GH17.hmm	311	27	306	0.90	6.400000e-26	443	684	GH17	GH17	GH	OTHER	0.001021	NaN	NaN	NaN	6743
4	priceomyces_haplophilus	-augustus-gene-0.127-mRNA-1_1_gene=maker-gi\|10...	GH17.hmm	311	2	310	0.99	2.600000e-44	23	304	GH17	GH17	GH	SP(Sec/SPI)	0.987767	18-19	ASA-IG	0.5361	4973

	organism	gene	hmm_model	hmm_model_len	hmm_match_from	hmm_match_to	hmm_match_coverage	match_evalue	gene_match_from	gene_match_to	enzyme	family	enzyme_type	signal_peptide	signal_peptide_prob	sp_cut_pos	sp_cut_seq	sp_cut_prob	genes_in_fasta
50314	metschnikowia_similis	snap_masked-sim-_s449-processed-gene-0.9-mRNA-...	GH3.hmm	216	4	216	0.98	6.900000e-62	35	247	GH3	GH3	GH	OTHER	0.005659	NaN	NaN	NaN	5629
50315	metschnikowia_similis	snap_masked-sim-_s48-processed-gene-0.52-mRNA-...	CE10.hmm	341	69	304	0.69	2.100000e-20	22	280	CE10	CE10	CE	OTHER	0.003821	NaN	NaN	NaN	5629
50316	metschnikowia_similis	snap_masked-sim-_s6-processed-gene-1.141-mRNA-...	GH17.hmm	311	15	141	0.41	1.600000e-24	346	473	GH17	GH17	GH	OTHER	0.000944	NaN	NaN	NaN	5629
50317	metschnikowia_similis	snap_masked-sim-_s6-processed-gene-1.97-mRNA-1...	GT57.hmm	481	3	480	0.99	1.700000e-140	68	547	GT57	GT57	GT	OTHER	0.000845	NaN	NaN	NaN	5629
50318	metschnikowia_similis	snap_masked-sim-_s78-processed-gene-0.49-mRNA-...	GH125.hmm	402	1	402	1.00	3.600000e-153	97	524	GH125	GH125	GH	OTHER	0.020790	NaN	NaN	NaN	5629

	hmm_model_len	hmm_match_from	hmm_match_to	hmm_match_coverage	match_evalue	gene_match_from	gene_match_to	signal_peptide_prob	sp_cut_prob	genes_in_fasta
count	50319.00000	50319.000000	50319.000000	50319.000000	5.031900e+04	50319.000000	50319.000000	50319.000000	14051.000000	50319.000000
mean	323.00630	16.102486	302.544764	0.895927	5.176759e-18	210.532284	513.428844	0.284458	0.661574	5721.081560
std	145.06825	44.105330	140.263727	0.174192	4.972978e-17	282.541051	326.321524	0.408095	0.223506	1023.163289
min	29.00000	1.000000	17.000000	0.350000	0.000000e+00	1.000000	23.000000	0.000042	0.069800	4128.000000
25%	233.00000	1.000000	216.000000	0.900000	2.100000e-126	39.000000	322.000000	0.001099	0.484000	5095.000000
50%	296.00000	3.000000	272.000000	0.980000	6.600000e-79	101.000000	409.000000	0.005924	0.700300	5557.000000
75%	389.00000	10.000000	366.000000	0.990000	1.600000e-35	272.000000	594.000000	0.728025	0.857000	6067.000000
max	943.00000	458.000000	862.000000	1.000000	9.800000e-16	4385.000000	4476.000000	0.999890	0.993300	11757.000000

	beta-glucan	cellulose	chitin	lignin	mannan	pectin	starch	xylan	xyloglucan
Name
Aciculoconidium aculeatum	10.0	9.0	67.0	7.0	12.0	0.0	8.0	9.0	16.0
Alloascoidea hylecoeti	13.0	31.0	19.0	9.0	23.0	11.0	12.0	19.0	39.0
Ambrosiozyma ambrosiae	10.0	18.0	10.0	9.0	18.0	1.0	13.0	16.0	26.0
Ambrosiozyma kashinagacola	4.0	9.0	7.0	8.0	12.0	0.0	9.0	8.0	14.0
Ambrosiozyma maleeae	4.0	9.0	8.0	7.0	11.0	0.0	8.0	8.0	12.0

	beta-glucan	cellulose	chitin	lignin	mannan	pectin	starch	xylan	xyloglucan
Name
Zygosaccharomyces bisporus	5.0	7.0	8.0	7.0	5.0	0.0	8.0	5.0	13.0
Zygosaccharomyces kombuchaensis	5.0	8.0	8.0	7.0	6.0	0.0	10.0	6.0	13.0
Zygosaccharomyces rouxii	3.0	9.0	6.0	6.0	6.0	0.0	9.0	6.0	12.0
Zygotorulaspora florentina	4.0	5.0	8.0	8.0	7.0	0.0	13.0	6.0	10.0
Zygotorulaspora mrakii	5.0	6.0	8.0	8.0	7.0	0.0	9.0	7.0	12.0

	beta-glucan	cellulose	chitin	lignin	mannan	pectin	starch	xylan	xyloglucan
count	332.000000	332.000000	332.000000	332.000000	332.000000	332.000000	332.000000	332.000000	332.000000
mean	7.234940	12.313253	9.701807	6.072289	10.822289	0.855422	10.210843	11.171687	16.816265
std	3.480491	6.241713	4.848608	1.759107	4.613953	1.874507	3.145630	4.990993	6.902820
min	1.000000	2.000000	3.000000	2.000000	2.000000	0.000000	5.000000	4.000000	5.000000
25%	4.000000	8.000000	7.000000	5.000000	8.000000	0.000000	8.000000	7.000000	12.000000
50%	7.000000	11.000000	9.000000	6.000000	10.000000	0.000000	10.000000	10.000000	16.000000
75%	10.000000	15.000000	11.000000	7.000000	13.000000	1.000000	11.250000	14.000000	20.000000
max	19.000000	37.000000	67.000000	14.000000	37.000000	15.000000	31.000000	35.000000	54.000000

	beta-glucan	cellulose	chitin	lignin	mannan	pectin	starch	xylan	xyloglucan
Name
aciculoconidium_aculeatum	10.0	9.0	67.0	7.0	12.0	0.0	8.0	9.0	16.0
alloascoidea_hylecoeti	13.0	31.0	19.0	9.0	23.0	11.0	12.0	19.0	39.0
ambrosiozyma_ambrosiae	10.0	18.0	10.0	9.0	18.0	1.0	13.0	16.0	26.0
ambrosiozyma_kashinagacola	4.0	9.0	7.0	8.0	12.0	0.0	9.0	8.0	14.0
ambrosiozyma_maleeae	4.0	9.0	8.0	7.0	11.0	0.0	8.0	8.0	12.0

	beta-glucan	cellulose	chitin	lignin	mannan	pectin	starch	xylan	xyloglucan
Name
zygosaccharomyces_bisporus	5.0	7.0	8.0	7.0	5.0	0.0	8.0	5.0	13.0
zygosaccharomyces_kombuchaensis	5.0	8.0	8.0	7.0	6.0	0.0	10.0	6.0	13.0
zygosaccharomyces_rouxii	3.0	9.0	6.0	6.0	6.0	0.0	9.0	6.0	12.0
zygotorulaspora_florentina	4.0	5.0	8.0	8.0	7.0	0.0	13.0	6.0	10.0
zygotorulaspora_mrakii	5.0	6.0	8.0	8.0	7.0	0.0	9.0	7.0	12.0